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International Conference on Intelligent Computing

ICIC 2016: Intelligent Computing Theories and Application pp 257–267Cite as

A Comparative Study for the Effects of Noise on Illumination Invariant Face Recognition Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9772))

Abstract

In the literature, the effects of noise on existing face recognition algorithms are neglected, to the best of our knowledge. In this paper, for the first time, we perform an experimental study for the effects of noise on existing illumination-invariant face recognition algorithms. In total, twenty-one algorithms have been included in this study in this paper. We find that, when noise is present in face images, Tan and Triggs’ method achieves the highest correct recognition rates for both the extended Yale B face database and the CMU-PIE face database. If face images do not contain noise, isotropic smoothing is preferred because this method obtains the highest average recognition rate (96 %) for the extended Yale B database and 16 out of 21 methods achieve 100 % correct recognition rates for the CMU-PIE face database.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, Concordia University, 1455 de Maisonneuve West, Montreal, QC, H3G 1M8, Canada

    Guangyi Chen

  2. Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve West, Montreal, QC, H3G 1M8, Canada

    Wenfang Xie

Authors
  1. Guangyi Chen
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  2. Wenfang Xie
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Correspondence to Guangyi Chen .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. University of Ulsan , Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Chen, G., Xie, W. (2016). A Comparative Study for the Effects of Noise on Illumination Invariant Face Recognition Algorithms. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_22

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  • DOI: https://doi.org/10.1007/978-3-319-42294-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42293-0

  • Online ISBN: 978-3-319-42294-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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